Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,890
2	New Jersey	19,955
3	Louisiana	16,828
4	Arizona	16,827
5	Rhode Island	16,341
6	Massachusetts	16,191
7	District of Columbia	15,369
8	Connecticut	13,263
9	Delaware	13,148
10	Florida	12,561
11	Illinois	12,259
12	Mississippi	12,192
13	Maryland	12,191
14	Iowa	11,223
15	South Carolina	11,002
16	Nebraska	10,966
17	Alabama	10,929
18	Georgia	10,208
19	Arkansas	9,399
20	Utah	9,217
21	Texas	9,132
22	Nevada	9,007
23	Tennessee	8,947
24	South Dakota	8,476
25	California	8,293
26	Virginia	8,279
27	North Carolina	8,192
28	Indiana	7,856
29	Pennsylvania	7,795
30	Michigan	7,703
31	Minnesota	7,503
32	New Mexico	7,167
33	Wisconsin	6,852
34	Kansas	6,623
35	Colorado	6,413
36	Idaho	6,139
37	North Dakota	5,692
38	Ohio	5,611
39	Washington	5,539
40	Oklahoma	5,113
41	Missouri	4,753
42	New Hampshire	4,452
43	Kentucky	4,423
44	Wyoming	3,217
45	Puerto Rico	3,022
46	Oregon	2,885
47	Maine	2,632
48	Alaska	2,422
49	West Virginia	2,368
50	Vermont	2,076
51	Montana	1,644
52	Hawaii	848

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	575
2	Arizona	444
3	Louisiana	439
4	South Carolina	385
5	Georgia	304
6	Mississippi	301
7	Alabama	300
8	Nevada	291
9	Texas	284
10	Idaho	256
11	Arkansas	255
12	Tennessee	223
13	Iowa	221
14	California	203
15	North Carolina	199
16	Utah	199
17	Oklahoma	146
18	Wisconsin	145
19	Kansas	144
20	Minnesota	125
21	New Mexico	123
22	Ohio	121
23	North Dakota	115
24	Alaska	114
25	Virginia	104
26	Indiana	102
27	Missouri	101
28	Nebraska	95
29	Delaware	93
30	Maryland	91
31	Montana	91
32	Kentucky	88
33	Illinois	83
34	Puerto Rico	82
35	District of Columbia	79
36	Washington	79
37	Oregon	77
38	West Virginia	77
39	Colorado	76
40	South Dakota	61
41	Michigan	56
42	Pennsylvania	56
43	Wyoming	50
44	New Jersey	38
45	New York	37
46	Massachusetts	33
47	Rhode Island	21
48	Hawaii	20
49	New Hampshire	19
50	Maine	13
51	Vermont	12
52	Connecticut	7

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,747
2	New York	1,646
3	Connecticut	1,219
4	Massachusetts	1,207
5	Rhode Island	921
6	District of Columbia	804
7	Louisiana	734
8	Michigan	632
9	Illinois	583
10	Maryland	548
11	Pennsylvania	542
12	Delaware	530
13	Mississippi	419
14	Indiana	409
15	Arizona	307
16	Colorado	299
17	New Hampshire	287
18	Georgia	277
19	Minnesota	273
20	Ohio	261
21	New Mexico	259
22	Iowa	238
23	Virginia	230
24	Alabama	228
25	Florida	197
26	Nevada	192
27	Washington	188
28	South Carolina	186
29	Missouri	181
30	California	178
31	Nebraska	153
32	Kentucky	145
33	North Carolina	145
34	Wisconsin	142
35	South Dakota	123
36	North Dakota	119
37	Texas	111
38	Tennessee	107
39	Arkansas	106
40	Oklahoma	106
41	Kansas	102
42	Vermont	89
43	Maine	84
44	Utah	67
45	Idaho	57
46	Oregon	56
47	West Virginia	53
48	Puerto Rico	52
49	Wyoming	36
50	Montana	27
51	Alaska	20
52	Hawaii	13

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	8
2	Mississippi	5
3	Louisiana	4
4	Alabama	3
5	Florida	3
6	South Carolina	3
7	South Dakota	3
8	Georgia	2
9	Massachusetts	2
10	Nevada	2
11	New Jersey	2
12	Texas	2
13	Arkansas	1
14	California	1
15	Colorado	1
16	Illinois	1
17	Indiana	1
18	Iowa	1
19	Maryland	1
20	Michigan	1
21	Montana	1
22	New Mexico	1
23	North Carolina	1
24	Ohio	1
25	Oklahoma	1
26	Pennsylvania	1
27	Tennessee	1
28	Utah	1
29	Virginia	1
30	Washington	1
31	Alaska	0
32	Connecticut	0
33	Delaware	0
34	District of Columbia	0
35	Hawaii	0
36	Idaho	0
37	Kansas	0
38	Kentucky	0
39	Maine	0
40	Minnesota	0
41	Missouri	0
42	Nebraska	0
43	New Hampshire	0
44	New York	0
45	North Dakota	0
46	Oregon	0
47	Puerto Rico	0
48	Rhode Island	0
49	Vermont	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	133,818	1	99
Lake	Tennessee	99,202	2	99
Lee	Arkansas	93,372	3	99
Dakota	Nebraska	90,333	4	99
Buena Vista	Iowa	88,583	5	99
Davidson	Tennessee	20,944	135	95
Richland	South Carolina	11,726	443	85
Orange	California	7,693	792	74
York	South Carolina	6,709	930	70
Pierce	Washington	3,949	1505	52

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,282	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	236	668	78
Davidson	Tennessee	206	734	76
Orange	California	133	991	68
Pierce	Washington	125	1018	67
York	South Carolina	43	1665	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.